Training complete

README.md

@@ -3,6 +3,8 @@ license: apache-2.0
 base_model: bert-base-cased
 tags:
 - generated_from_trainer
+datasets:
+- conll2002
 metrics:
 - precision
 - recall
@@ -10,7 +12,29 @@ metrics:
 - accuracy
 model-index:
 - name: bert-finetuned-ner
-  results: []
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2002
+      type: conll2002
+      config: es
+      split: validation
+      args: es
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.7640546993705232
+    - name: Recall
+      type: recall
+      value: 0.8088235294117647
+    - name: F1
+      type: f1
+      value: 0.7858019868288871
+    - name: Accuracy
+      type: accuracy
+      value: 0.9676902769959431
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -18,13 +42,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # bert-finetuned-ner
 
-This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
+This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2002 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1471
-- Precision: 0.7369
-- Recall: 0.7943
-- F1: 0.7646
-- Accuracy: 0.9666
+- Loss: 0.1912
+- Precision: 0.7641
+- Recall: 0.8088
+- F1: 0.7858
+- Accuracy: 0.9677
 
 ## Model description
 
@@ -49,22 +73,27 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 5
+- num_epochs: 10
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.1691        | 1.0   | 521  | 0.1438          | 0.6830    | 0.7371 | 0.7090 | 0.9587   |
-| 0.076         | 2.0   | 1042 | 0.1402          | 0.7075    | 0.7670 | 0.7361 | 0.9622   |
-| 0.05          | 3.0   | 1563 | 0.1332          | 0.7536    | 0.7971 | 0.7748 | 0.9672   |
-| 0.0359        | 4.0   | 2084 | 0.1442          | 0.7420    | 0.7845 | 0.7626 | 0.9663   |
-| 0.0265        | 5.0   | 2605 | 0.1471          | 0.7369    | 0.7943 | 0.7646 | 0.9666   |
+| 0.1713        | 1.0   | 521  | 0.1404          | 0.6859    | 0.7387 | 0.7114 | 0.9599   |
+| 0.0761        | 2.0   | 1042 | 0.1404          | 0.6822    | 0.7693 | 0.7231 | 0.9623   |
+| 0.05          | 3.0   | 1563 | 0.1304          | 0.7488    | 0.7937 | 0.7706 | 0.9672   |
+| 0.0355        | 4.0   | 2084 | 0.1454          | 0.7585    | 0.7960 | 0.7768 | 0.9664   |
+| 0.0253        | 5.0   | 2605 | 0.1501          | 0.7549    | 0.8095 | 0.7812 | 0.9677   |
+| 0.0184        | 6.0   | 3126 | 0.1726          | 0.7581    | 0.7992 | 0.7781 | 0.9662   |
+| 0.0138        | 7.0   | 3647 | 0.1743          | 0.7524    | 0.8042 | 0.7774 | 0.9676   |
+| 0.0112        | 8.0   | 4168 | 0.1853          | 0.7576    | 0.8022 | 0.7792 | 0.9674   |
+| 0.0082        | 9.0   | 4689 | 0.1914          | 0.7595    | 0.8061 | 0.7821 | 0.9667   |
+| 0.0073        | 10.0  | 5210 | 0.1912          | 0.7641    | 0.8088 | 0.7858 | 0.9677   |
 
 
 ### Framework versions
 
-- Transformers 4.40.2
-- Pytorch 2.2.1+cu121
+- Transformers 4.41.0
+- Pytorch 2.3.0+cu121
 - Datasets 2.19.1
 - Tokenizers 0.19.1